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Fine-tuning a BERT model for search applications
How to ensure training and serving encoding compatibility.
From research to production: scaling a state-of-the-art machine learning system
How we implemented a production-ready question-answering application and reduced response time by more than two orders of magnitude.
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Fine-tuning a BERT model with transformers
Setup a custom Dataset, fine-tune BERT with Transformers Trainer and export the model via ONNX.
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Vespa Product Updates, October 2020
Improvement to Vespa feeding APIs
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Vespa Product Updates, September 2020
Introducing ONNX-Runtime, Hamming Distance Metric, Conditional Update Performance Improvements and Compressed Transaction Log with Synced Ack
Efficient open-domain question-answering on Vespa.ai
In this post, we reproduce the state-of-the-art baseline for retrieval-based question-answering systems within a single, scalable production ready application on Vespa.ai.
Vespa Product Updates, August 2020
Introducing NLP with Transformers, Grafana how-to, Improved GEO Search Support, Query Profile Variants Optimizations, & Build on Debian 10
Vespa Product Updates, June 2020
Announcing support for approximate nearest neighbor vector search which can be combined with filters and text search with state-of-the art performance
Introducing NLP with Transformers on Vespa
We’ve been working a lot lately on evaluating Transformer models in Vespa. Here we show how and share a bit on how we view the benefits of inference in Vespa....
Approximate Nearest Neighbor Search in Vespa - Part 1
In this blog post we explore how the Vespa team selected HNSW (Hierarchical Navigable Small World Graphs) as the baseline approximate nearest neighbor algorithm for extension and integration in Vespa....
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